"""simple docstring"""



SCREAMING_SNAKE_CASE__:Any									=			8.314_462  # Unit - J mol-1 K-1



def 	_lowerCamelCase(	a   ,    a   ,    a				):
       if moles < 0 or kelvin < 0 or volume < 0:
              raise ValueError("Invalid inputs. Enter positive value."				)
       return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume



def 	_lowerCamelCase(	a   ,    a   ,    a				):
       if moles < 0 or kelvin < 0 or pressure < 0:
              raise ValueError("Invalid inputs. Enter positive value."				)
       return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure


if __name__ == "__main__":
   from doctest import testmod

   testmod()

"""simple docstring"""



from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
SCREAMING_SNAKE_CASE__:str									=			HfArgumentParser(InitializationArguments)
SCREAMING_SNAKE_CASE__:List[str]									=			parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
SCREAMING_SNAKE_CASE__:List[Any]									=			AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
SCREAMING_SNAKE_CASE__:Union[str, Any]									=			{
    """vocab_size""": len(tokenizer),
    """scale_attn_by_inverse_layer_idx""": True,
    """reorder_and_upcast_attn""": True,
}

# Load model config (GPT-2 large in this case)
SCREAMING_SNAKE_CASE__:Optional[int]									=			AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
SCREAMING_SNAKE_CASE__:Tuple									=			AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)
